ArticleJournal of exposure science & environmental epidemiology2023
Microenvironment Tracker (MicroTrac) model to estimate time-location of individuals for air pollution exposure assessments: model evaluation using smartphone data.
Article in Journal of exposure science & environmental epidemiology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed, 9 citations in OpenAlex.
- Precision greenness exposure is associated with lung cancer risk in medicare beneficiaries.Cancer causes & control : CCC · 2026Article
- TracMyAir: Smartphone-enabled spatiotemporal estimates for inhaled doses of particulate matter and ozone to personalize health outcomes.medRxiv : the preprint server for health sciences · 2026Article
- TracMyAir smartphone application for modeling exposures to PMThe Science of the total environment · 2025Article
- Longitudinal Impacts of High Versus Low Greenness on Cardiovascular Disease Conditions.Journal of the American Heart Association · 2024Article
- Longitudinal Impacts of Precision Greenness on Alzheimer's Disease.The journal of prevention of Alzheimer's disease · 2024Article
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Authors and funding
5 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundA critical aspect of air pollution exposure assessments is determining the time spent in various microenvironments (ME), which can have substantially different pollutant concentrations. We previously developed and evaluated a ME classification model, called Microenvironment Tracker (MicroTrac), to estimate time of day and duration spent in eight MEs (indoors and outdoors at home, work, school; inside vehicles; other locations) based on input data from global positioning system (GPS) loggers.
objectiveIn this study, we extended MicroTrac and evaluated the ability of using geolocation data from smartphones to determine the time spent in the MEs.
methodWe performed a panel study, and the MicroTrac estimates based on data from smartphones and GPS loggers were compared to 37 days of diary data across five participants.
resultsThe MEs were correctly classified for 98.1% and 98.3% of the time spent by the participants using smartphones and GPS loggers, respectively. SIGNIFICANCE: Our study demonstrates the extended capability of using ubiquitous smartphone data with MicroTrac to help reduce time-location uncertainty in air pollution exposure models for epidemiologic and exposure field studies.
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